Bayesian Tracking of Neural Activity in Biomagnetic Data

Abstract

Magnetoencephalography (MEG) is a non-invasive brain imaging tecnique measuring the weak magnetic field due to neural activity. The analysis of the temporal evolution
of the magnetic field, however, does not provide accurate spatial information about the neural activations in the cerebral cortex. Such information can be restored only by solving
the inverse problem. We propose a probabilistic approach to solve this problem: a particle filter is implemented to realize a Bayesian tracking of the brain sources, modeled as pointwise
currents.